D465 Data Applications
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Free D465 Data Applications Questions
What is the primary focus of Natural Language Processing (NLP) in the context of data science?
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Analyzing and modeling human language
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Visualizing data trends
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Predicting future outcomes
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Cleaning and transforming data
Explanation
Explanation:
In data science, Natural Language Processing (NLP) focuses on enabling computers to analyze and model human language in a way that allows meaningful interaction and interpretation. NLP techniques are designed to process unstructured text data—such as documents, emails, or social media posts—to extract insights, detect sentiment, and recognize entities or intent. It combines computational linguistics, machine learning, and deep learning to understand the structure, grammar, and semantics of language. This makes it a vital tool for applications such as chatbots, text summarization, speech recognition, and translation.
Correct Answer:
Analyzing and modeling human language
A data analyst finds the code mdy(10211020) in an R script. What is the year of the date that is created?
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1021
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1020
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2120
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1102
Explanation
Explanation:
The mdy() function from the lubridate package in R interprets numbers or strings in month-day-year format. In the code mdy(10211020), the last four digits represent the year, which is 1020. The function converts the numeric input into a date object, correctly parsing the month and day from the first part and assigning the year from the last four digits.
Correct Answer:
1020
Explain how predictive modeling differs from data mining in the context of data science.
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Predictive modeling focuses on future outcomes, while data mining analyzes historical data.
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Data mining is used for real-time analysis, whereas predictive modeling is not.
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Both techniques are identical and serve the same purpose
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Predictive modeling requires no data, while data mining does
Explanation
Explanation:
Predictive modeling and data mining serve different purposes in data science. Predictive modeling uses historical data to build models that forecast future outcomes, such as predicting customer behavior or equipment failure. Data mining, on the other hand, focuses on exploring and analyzing historical data to uncover patterns, relationships, or trends. While predictive modeling often relies on insights from data mining, the key distinction is that predictive modeling is future-oriented, whereas data mining is historical and descriptive.
Correct Answer:
Predictive modeling focuses on future outcomes, while data mining analyzes historical data.
Explain how data science can be utilized for injury prediction in sports. What types of data might be analyzed?
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By analyzing player performance metrics and historical injury data
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By monitoring fan engagement on social media
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By evaluating ticket sales trends
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By assessing weather conditions during games
Explanation
Explanation:
Data science can be used to predict sports injuries by analyzing detailed datasets, including player performance metrics, training loads, biometrics, and historical injury records. Machine learning models can identify patterns or risk factors that increase the likelihood of injury, enabling coaches and medical staff to implement preventive measures. This proactive approach helps optimize player health and team performance, unlike fan engagement or ticket sales data, which do not provide insights into injury risk.
Correct Answer:
By analyzing player performance metrics and historical injury data
Algorithmic trading helps fund managers with market pricing by
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Analysing the economic data
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Exercising market timing
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Identifying profit opportunities arising from market anomalies
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Identifying the buying and selling opportunities
Explanation
Explanation:
Algorithmic trading uses computer algorithms to analyze market data and execute trades. Its primary benefit is identifying profit opportunities arising from market anomalies, such as price discrepancies, liquidity gaps, or arbitrage opportunities. This allows fund managers to make rapid, data-driven decisions in financial markets, improving returns while minimizing human error. While economic analysis or market timing may contribute, the focus of algorithmic trading is exploiting short-term market inefficiencies.
Correct Answer:
Identifying profit opportunities arising from market anomalies
A sports team wants to enhance fan engagement through data science. Which strategy would be most effective based on data analysis techniques?
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Implementing a loyalty program based on ticket purchases
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Using machine learning to analyze social media sentiment about the team
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Increasing the number of games played in a season
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Reducing ticket prices for all fans
Explanation
Explanation:
Using machine learning to analyze social media sentiment about the team allows the organization to understand fans’ emotions, opinions, and engagement levels in real time. By analyzing posts, comments, and reactions, the team can gauge public perception and identify what drives positive or negative fan responses. These insights can then guide marketing strategies, improve fan experiences, and tailor content to increase loyalty and enthusiasm among supporters.
Correct Answer:
Using machine learning to analyze social media sentiment about the team
A data analyst is creating a plot for a presentation to stakeholders. The analyst wants to add a caption to the plot to help communicate important information. What function could the analyst use?
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The geom_point() function
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The facet_wrap() function
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The labs() function
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The geom_bar() function
Explanation
Explanation:
The labs() function in ggplot2 is used to add labels, titles, and captions to a plot. By specifying the caption argument within labs(), an analyst can provide context, highlight key insights, or communicate important information directly on the visualization. This enhances the interpretability of the plot for stakeholders and makes the visual presentation more effective.
Correct Answer:
The labs() function
In the field of transportation, what is one of the most valuable uses of big data analytics?
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Predicting traffic congestion and optimizing route planning
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Storing transport schedules in spreadsheets
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Reducing data collection from GPS systems
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Analyzing only past traffic without considering real-time updates
Explanation
Explanation:
Big data analytics in transportation allows for the prediction of traffic congestion and route optimization by processing large volumes of real-time data from GPS sensors, traffic cameras, and vehicle tracking systems. By analyzing these data streams, transportation agencies can anticipate bottlenecks, manage traffic flow, and recommend optimal routes to drivers. This improves travel efficiency and reduces fuel consumption and emissions.
Correct Answer:
Predicting traffic congestion and optimizing route planning
What is the primary function of prescriptive analytics in data science?
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To analyze historical data
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To recommend actions for desired outcomes
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To visualize data trends
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To clean and transform raw data
Explanation
Explanation:
Prescriptive analytics goes beyond descriptive and predictive analytics by recommending specific actions to achieve desired outcomes. It uses data, models, and algorithms to suggest the best course of action for decision-making. While analyzing historical data and visualizing trends are important, prescriptive analytics specifically focuses on providing actionable recommendations rather than just insights or forecasts.
Correct Answer:
To recommend actions for desired outcomes
Explain why programming is a critical skill in data science, particularly in the context of data manipulation and analysis.
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It allows for the creation of user interfaces.
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It enables the automation of repetitive tasks
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It provides tools for data visualization only.
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It is only necessary for statistical analysis
Explanation
Explanation:
Programming is essential in data science because it enables data scientists to efficiently manipulate, clean, and analyze large datasets. Through programming languages such as Python, R, or SQL, data scientists can automate repetitive tasks, perform complex calculations, and build scalable workflows for data processing. This skill also allows for integration of multiple data sources, the creation of reusable scripts, and the implementation of machine learning algorithms. Beyond automation, programming facilitates reproducibility and accuracy in data analysis, ensuring that insights and models are both reliable and adaptable to new data.
Correct Answer:
It enables the automation of repetitive tasks
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